Economic cointegration of the North American agricultural sector
Bibliographic record
Abstract
Objective: To determine if the Mexican, Canadian, and American agricultural industries are cointegrated. Methodology: Six cointegration tests were carried out between the Mexican, Canadian, and American agricultural, forestry, fishing, and hunting industries, as well as the Mexican animal husbandry and exploitation sector. The USA was the independent variable in all cases. Results: The Mexican sector, with an α of 5% (with and without trend) is not cointegrated with the USA and Canada, while Canada and the USA, with an α of 5% (with and without trend) are cointegrated. Study Limitations/Implications: The agricultural sector of the three countries were not analyzed separately and the Engle-Granger causality test was not used. Although some products from Mexico's agricultural sector have managed to make inroads in the USA and Canada, further advances are still possible. Therefore, there are areas of improvement for Mexican products. Likewise, NAFTA and the USMCA/CUSMA have failed to achieve their objective of cointegrating the agricultural sectors of the three nations. Conclusions: The Mexican sector was not cointegrated with the American and Canadian sectors during the analysis period —i.e., the Mexican sector is not influenced by and does not have the same long-term behavior (with delays) than the USA and Canadian sectors. However, the Canadian sector is cointegrated with the USA sector —i.e., the Canadian sector is influenced and has the same long-term behavior than the USA sector.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".